The AI Bill of Rights is the first step toward similar regulation of AI and machine learning algorithms

Within my AI predictions for the year, interpretable machine learning (ML) models have surprised me by garnering greater awareness from data science leaders

Helping organizations navigate the often-challenging implementation of Responsible AI as part of ESG efforts

New AML scores reduce false positive alerts by 50% while detecting 100% of known money laundering transactions, and discover new aberrant, potentially risky behaviors

Many organizations, including data science teams, derail innovation with internal competition that fragments resources and energy.

I predict that Auditable AI and Humble AI will become widely used in 2022, as essential parts of Responsible AI

FICO’s data science labs called interpretable latent feature-based neural networks (ILF NNET) help expose and eliminate biases derived from data showing up in the model.

The Responsible AI Playbook provides a clear path to operationalizing AI deployments. Use it to achieve high-impact business results within important ethical and legal boundaries.

Data scientists need to justify the need for the incremental risk we assume when using more complicated methods to solve a problem - not be open source junkies

Auditable AI provides the documentation and records necessary to pass a regulatory review

FICO’s new Scam Detection Score identifies 50% more scam transactions compared to the standard fraud score

New "State of Responsible AI" report from Corinium and FICO finds that most companies don’t—and are deploying artificial intelligence at significant risk

There is an urgent need for open banking fraud detection, even before banks have sufficient data to train supervised fraud detection models

In part 2 of this series, I take a deep dive into the data science that drives monotonicity and palatability, qualities that help gain trust in AI

Now there is a better way to reduce SAR false positives while stopping money laundering